Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion Models

TL;DR

This paper introduces a diffusion model conditioned on redshift that captures galaxy morphology evolution, enabling redshift estimation from imaging data and revealing a direct link between galaxy shape and cosmic distance.

Contribution

The study presents the first diffusion model conditioned on redshift that learns galaxy morphology evolution and correlates with true redshifts, improving redshift estimation from imaging data.

Findings

Model reproduces morphology-redshift correlations.

Captures trends in ellipticity, size, Sersic index, and isophotal area.

Close correlation with true redshifts on test data.

Abstract

Redshift measures the distance to galaxies and underlies our understanding of the origin of the Universe and galaxy evolution. Spectroscopic redshift is the gold-standard method for measuring redshift, but it requires about $1000$ times more telescope time than broad-band imaging. That extra cost limits sky coverage and sample size and puts large spectroscopic surveys out of reach. Photometric redshift methods rely on imaging in multiple color filters and template fitting, yet they ignore the wealth of information carried by galaxy shape and structure. We demonstrate that a diffusion model conditioned on continuous redshift learns this missing joint structure, reproduces known morphology-$z$ correlations. We verify on the HyperSuprime-Cam survey, that the model captures redshift-dependent trends in ellipticity, semi-major axis, S\'ersic index, and isophotal area that these generated images correlate closely with true redshifts on test data. To our knowledge this is the first study to establish a direct link between galaxy morphology and redshift. Our approach offers a simple and effective path to redshift estimation from imaging data and will help unlock the full potential of upcoming wide-field surveys.